Metadata-Version: 2.1
Name: napari-spotiflow
Version: 0.3.2
Summary: Napari plugin for Spotiflow
Home-page: https://github.com/weigertlab/napari-spotiflow
Author: Albert Dominguez Mantes, Martin Weigert
Author-email: albert.dominguezmantes@epfl.ch, martin.weigert@epfl.ch
License: BSD 3-Clause License
Project-URL: Bug Tracker, https://github.com/weigertlab/napari-spotiflow/issues
Project-URL: Documentation, https://github.com/weigertlab/napari-spotiflow#README.md
Project-URL: Source Code, https://github.com/weigertlab/napari-spotiflow
Project-URL: User Support, https://github.com/weigertlab/napari-spotiflow/issues
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Framework :: napari
Classifier: Topic :: Software Development :: Testing
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: BSD License
Requires-Python: <3.12,>=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: spotiflow
Requires-Dist: npe2
Requires-Dist: napari

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![Logo](https://github.com/weigertlab/napari-spotiflow/raw/main/artwork/spotiflow_logo.png)
---

# Spotiflow: napari plugin

Napari plugin for *Spotiflow*, a deep learning-based, threshold-agnostic, and subpixel-accurate spot detection method for fluorescence microscopy. The plugin allows using several pre-trained models as well as user-trained ones. For the main repository, see [here](https://github.com/weigertlab/spotiflow). 

https://github.com/weigertlab/napari-spotiflow/assets/11042162/02940480-daa9-4a21-8cf5-ad73c26c9838

If you use this plugin for your research, please [cite us](https://github.com/weigertlab/spotiflow#how-to-cite).

----------------------------------

## Installation

The plugin can be installed directly from PyPi (make sure you use a conda environment with `napari` and `spotiflow` installed):

```
pip install napari-spotiflow
```

## Usage 

1. Open the image (or open one of our samples, _e.g._ `File > Open Sample > napari-spotiflow > HybISS`)
2. Start the plugin `Plugins > napari-spotiflow`
3. Select model (pre-trained or custom trained) and optionally adjust any other parameters
4. Click `Run`

## Supported input formats
- 2D (YX, YXC or CYX)
- 2D+t (TYX, TYXC or TCYX)

## How to cite
See the [main repository's _How to cite_ section](https://github.com/weigertlab/spotiflow?tab=readme-ov-file#how-to-cite).
